A new open-source project demonstrates how to combine computer vision and robotics on a single board. The robot, about the size of a desk, detects a human face through locally executed machine learning and turns toward it in real time, working at roughly 10–15 frames per second. Everything runs on the new Arduino UNO Q, without relying on the cloud.

Dual-processor architecture

The operation leverages the hybrid architecture of the UNO Q, which integrates two distinct units. The Qualcomm MPU microprocessor, running Linux, executes the AI model and the control logic written in Python. The STM32 MCU microcontroller, running Zephyr RTOS, drives the PWM signal of the servomotors with real-time precision.

The two processors communicate via Bridge RPC, a protocol based on MessagePack over an internal serial link, with a round-trip latency of about 8 milliseconds. This way, heavy inference stays separate from the real-time path, keeping motor control deterministic.

Vision, control, and web interface

A USB webcam captures video, while a lightweight face-detection model locates the face in the frame. A proportional controller then converts the horizontal position of the face into differential commands for the wheels. The project uses two ready-made App Lab “bricks”: one for camera-based object detection, the other for the web interface.

In particular, the browser-accessible dashboard lets you monitor detections and adjust steering parameters in real time, without recompiling the code. Every change to the sliders takes effect immediately via Socket.IO, making the tuning phase quick.

Technical details and motion safety

The robot uses differential drive with two continuous-rotation servos: a 1500-microsecond pulse corresponds to stop, while lower or higher values determine rotation in either direction. Additionally, the system limits Bridge calls to a maximum of 20 Hz, because sending commands too quickly would block the serial link.

The project therefore includes several protections: a “coasting” mechanism that maintains the last known position when the face disappears for a moment, a watchdog that stops the robot if detections are interrupted, and an emergency stop that ignores rate limiting to halt the motors immediately. Classified as an intermediate-level project, it is fully documented and released under an open-source license.

The post Face-Following Robot Built on Arduino UNO Q and Edge Impulse appeared first on Open Electronics.

“There goes the neighborhood” isn’t a phrase to be thrown about lightly, but when they build a police station next door to your house, you know things are about to get noisy. Just how bad it’ll be is perhaps a bit subjective, with pleas for relief likely to fall on deaf ears unless you’ve got firm documentation like that provided by this automated noise detection system.

OK, let’s face it — even with objective proof there’s likely nothing that [Christopher Cooper] is going to do about the new crop of sirens going off in his neighborhood. Emergencies require a speedy response, after all, and sirens are perhaps just the price that we pay to live close to each other. That doesn’t mean there’s no reason to monitor the neighborhood noise, though, so [Christopher] got to work. The system uses an Arduino BLE Sense module to detect neighborhood noises and Edge Impulse to classify the sounds. An ESP32 does most of the heavy lifting, including running the UI on a nice little TFT touchscreen.

When a siren-like sound is detected, the sensor records the event and tries to classify the type of siren — fire, police, or ambulance. You can also manually classify sounds the system fails to understand, and export a summary of events to an SD card. If your neighborhood noise problems tend more to barking dogs or early-morning leaf blowers, no problem — you can easily train different models.

While we can’t say that this will help keep the peace in his neighborhood, we really like the way this one came out. We’ve seen the BLE Sense and Edge Impulse team up before, too, for everything from tuning a bike suspension to calming a nervous dog.